metadata
language:
- en
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- QEC
metrics:
- wer
model-index:
- name: whisper-small-quartr
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Quartr Earnings Calls
type: QEC
args: 'config: en, split: test'
metrics:
- type: wer
value: 32.834424695977546
name: Wer
whisper-small-quartr
This model is a fine-tuned version of openai/whisper-small on the Quartr Earnings Calls dataset. It achieves the following results on the evaluation set:
- Loss: 0.6429
- Wer: 32.8344
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.120528078446462e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 84
- training_steps: 1500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6771 | 0.32 | 100 | 0.6577 | 25.7437 |
| 0.6533 | 0.64 | 200 | 0.6025 | 34.6804 |
| 0.5793 | 0.96 | 300 | 0.5784 | 24.3530 |
| 0.3872 | 1.28 | 400 | 0.5856 | 32.9592 |
| 0.4447 | 1.61 | 500 | 0.5646 | 23.2429 |
| 0.414 | 1.93 | 600 | 0.5616 | 70.7016 |
| 0.2489 | 2.25 | 700 | 0.5816 | 35.2666 |
| 0.2863 | 2.57 | 800 | 0.5853 | 24.7583 |
| 0.2698 | 2.89 | 900 | 0.5844 | 32.7409 |
| 0.1646 | 3.21 | 1000 | 0.6182 | 27.7892 |
| 0.174 | 3.53 | 1100 | 0.6228 | 36.2021 |
| 0.2021 | 3.85 | 1200 | 0.6269 | 35.9775 |
| 0.2239 | 4.17 | 1300 | 0.6367 | 35.2666 |
| 0.1551 | 4.49 | 1400 | 0.6420 | 32.4478 |
| 0.1351 | 4.82 | 1500 | 0.6429 | 32.8344 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.2